{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70bfeb06",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "663830f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_palette = sns.color_palette(['#E24A33', '#348ABD', '#988ED5', '#777777', '#FBC15E', '#8EBA42', '#FFB5B8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "111592ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Co-occurence In</th>\n",
       "      <th>SMILES A</th>\n",
       "      <th>SMILES B</th>\n",
       "      <th>cFP edit distance value</th>\n",
       "      <th>cFP edit distance</th>\n",
       "      <th>tanimoto distance value</th>\n",
       "      <th>tanimoto distance</th>\n",
       "      <th>POM distance value</th>\n",
       "      <th>POM distance</th>\n",
       "      <th>Carey et al. neural distance value</th>\n",
       "      <th>Carey et al. neural distance</th>\n",
       "      <th>Hallem and Carlson neural distance value</th>\n",
       "      <th>Hallem and Carlson neural distance</th>\n",
       "      <th>Chae et al. neural distance value</th>\n",
       "      <th>Chae et al. neural distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC(C)[C@@H]1CC[C@@H](C)CC1=O</td>\n",
       "      <td>CCCCCCCCCC(=O)OCC</td>\n",
       "      <td>81.0</td>\n",
       "      <td>19017</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>17523</td>\n",
       "      <td>1.318330</td>\n",
       "      <td>20023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spearmint, Mentha spicata crispa, extract, Men...</td>\n",
       "      <td>CC(C)[C@@H]1CC[C@@H](C)CC1=O</td>\n",
       "      <td>CC(C)=CCCC(C)(O)C=C</td>\n",
       "      <td>61.0</td>\n",
       "      <td>9893</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>21593</td>\n",
       "      <td>1.065346</td>\n",
       "      <td>11574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC(C)[C@@H]1CC[C@@H](C)CC1=O</td>\n",
       "      <td>COc1ccc(CC=C)cc1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>12857</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>13444</td>\n",
       "      <td>1.185299</td>\n",
       "      <td>15507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC(C)[C@@H]1CC[C@@H](C)CC1=O</td>\n",
       "      <td>C[C@H]1CC[C@H](CC2=C1CC[C@@H]2C)C(C)(C)OC(C)=O</td>\n",
       "      <td>77.0</td>\n",
       "      <td>17650</td>\n",
       "      <td>0.822222</td>\n",
       "      <td>3476</td>\n",
       "      <td>0.878223</td>\n",
       "      <td>6736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC(C)[C@@H]1CC[C@@H](C)CC1=O</td>\n",
       "      <td>CNc1ccccc1C(=O)OC</td>\n",
       "      <td>66.0</td>\n",
       "      <td>12498</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>13996</td>\n",
       "      <td>1.167518</td>\n",
       "      <td>14915</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22786</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC(/CO)=C\\CC[C@]1(C)[C@H]2CC[C@H](C2)C1=C</td>\n",
       "      <td>CC(=O)OC(C)(C)C1CCC(=CC1)C</td>\n",
       "      <td>81.0</td>\n",
       "      <td>18954</td>\n",
       "      <td>0.883333</td>\n",
       "      <td>7444</td>\n",
       "      <td>0.886487</td>\n",
       "      <td>6901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22787</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC(/CO)=C\\CC[C@]1(C)[C@H]2CC[C@H](C2)C1=C</td>\n",
       "      <td>CCCCCCCCCC(C)O</td>\n",
       "      <td>88.0</td>\n",
       "      <td>21022</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>15431</td>\n",
       "      <td>0.790179</td>\n",
       "      <td>5106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22788</th>\n",
       "      <td>Cedar leaf oil, China</td>\n",
       "      <td>CC1(C)C2CCC(C)(C2)C1=O</td>\n",
       "      <td>CC(=O)OC(C)(C)C1CCC(=CC1)C</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8496</td>\n",
       "      <td>0.872340</td>\n",
       "      <td>6478</td>\n",
       "      <td>1.040808</td>\n",
       "      <td>10838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22789</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1(C)C2CCC(C)(C2)C1=O</td>\n",
       "      <td>CCCCCCCCCC(C)O</td>\n",
       "      <td>73.0</td>\n",
       "      <td>15765</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>20907</td>\n",
       "      <td>1.250822</td>\n",
       "      <td>17712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22790</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CC(=O)OC(C)(C)C1CCC(=CC1)C</td>\n",
       "      <td>CCCCCCCCCC(C)O</td>\n",
       "      <td>85.0</td>\n",
       "      <td>20267</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>18537</td>\n",
       "      <td>0.927156</td>\n",
       "      <td>7736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22791 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Co-occurence In  \\\n",
       "0                                                    NaN   \n",
       "1      Spearmint, Mentha spicata crispa, extract, Men...   \n",
       "2                                                    NaN   \n",
       "3                                                    NaN   \n",
       "4                                                    NaN   \n",
       "...                                                  ...   \n",
       "22786                                                NaN   \n",
       "22787                                                NaN   \n",
       "22788                              Cedar leaf oil, China   \n",
       "22789                                                NaN   \n",
       "22790                                                NaN   \n",
       "\n",
       "                                        SMILES A  \\\n",
       "0                   CC(C)[C@@H]1CC[C@@H](C)CC1=O   \n",
       "1                   CC(C)[C@@H]1CC[C@@H](C)CC1=O   \n",
       "2                   CC(C)[C@@H]1CC[C@@H](C)CC1=O   \n",
       "3                   CC(C)[C@@H]1CC[C@@H](C)CC1=O   \n",
       "4                   CC(C)[C@@H]1CC[C@@H](C)CC1=O   \n",
       "...                                          ...   \n",
       "22786  CC(/CO)=C\\CC[C@]1(C)[C@H]2CC[C@H](C2)C1=C   \n",
       "22787  CC(/CO)=C\\CC[C@]1(C)[C@H]2CC[C@H](C2)C1=C   \n",
       "22788                     CC1(C)C2CCC(C)(C2)C1=O   \n",
       "22789                     CC1(C)C2CCC(C)(C2)C1=O   \n",
       "22790                 CC(=O)OC(C)(C)C1CCC(=CC1)C   \n",
       "\n",
       "                                             SMILES B  \\\n",
       "0                                   CCCCCCCCCC(=O)OCC   \n",
       "1                                 CC(C)=CCCC(C)(O)C=C   \n",
       "2                                    COc1ccc(CC=C)cc1   \n",
       "3      C[C@H]1CC[C@H](CC2=C1CC[C@@H]2C)C(C)(C)OC(C)=O   \n",
       "4                                   CNc1ccccc1C(=O)OC   \n",
       "...                                               ...   \n",
       "22786                      CC(=O)OC(C)(C)C1CCC(=CC1)C   \n",
       "22787                                  CCCCCCCCCC(C)O   \n",
       "22788                      CC(=O)OC(C)(C)C1CCC(=CC1)C   \n",
       "22789                                  CCCCCCCCCC(C)O   \n",
       "22790                                  CCCCCCCCCC(C)O   \n",
       "\n",
       "       cFP edit distance value  cFP edit distance  tanimoto distance value  \\\n",
       "0                         81.0              19017                 0.951220   \n",
       "1                         61.0               9893                 0.976744   \n",
       "2                         67.0              12857                 0.926829   \n",
       "3                         77.0              17650                 0.822222   \n",
       "4                         66.0              12498                 0.930233   \n",
       "...                        ...                ...                      ...   \n",
       "22786                     81.0              18954                 0.883333   \n",
       "22787                     88.0              21022                 0.940000   \n",
       "22788                     58.0               8496                 0.872340   \n",
       "22789                     73.0              15765                 0.973684   \n",
       "22790                     85.0              20267                 0.956522   \n",
       "\n",
       "       tanimoto distance  POM distance value  POM distance  \\\n",
       "0                  17523            1.318330         20023   \n",
       "1                  21593            1.065346         11574   \n",
       "2                  13444            1.185299         15507   \n",
       "3                   3476            0.878223          6736   \n",
       "4                  13996            1.167518         14915   \n",
       "...                  ...                 ...           ...   \n",
       "22786               7444            0.886487          6901   \n",
       "22787              15431            0.790179          5106   \n",
       "22788               6478            1.040808         10838   \n",
       "22789              20907            1.250822         17712   \n",
       "22790              18537            0.927156          7736   \n",
       "\n",
       "       Carey et al. neural distance value  Carey et al. neural distance  \\\n",
       "0                                     NaN                           NaN   \n",
       "1                                     NaN                           NaN   \n",
       "2                                     NaN                           NaN   \n",
       "3                                     NaN                           NaN   \n",
       "4                                     NaN                           NaN   \n",
       "...                                   ...                           ...   \n",
       "22786                                 NaN                           NaN   \n",
       "22787                                 NaN                           NaN   \n",
       "22788                                 NaN                           NaN   \n",
       "22789                                 NaN                           NaN   \n",
       "22790                                 NaN                           NaN   \n",
       "\n",
       "       Hallem and Carlson neural distance value  \\\n",
       "0                                           NaN   \n",
       "1                                           NaN   \n",
       "2                                           NaN   \n",
       "3                                           NaN   \n",
       "4                                           NaN   \n",
       "...                                         ...   \n",
       "22786                                       NaN   \n",
       "22787                                       NaN   \n",
       "22788                                       NaN   \n",
       "22789                                       NaN   \n",
       "22790                                       NaN   \n",
       "\n",
       "       Hallem and Carlson neural distance  Chae et al. neural distance value  \\\n",
       "0                                     NaN                                NaN   \n",
       "1                                     NaN                                NaN   \n",
       "2                                     NaN                                NaN   \n",
       "3                                     NaN                                NaN   \n",
       "4                                     NaN                                NaN   \n",
       "...                                   ...                                ...   \n",
       "22786                                 NaN                                NaN   \n",
       "22787                                 NaN                                NaN   \n",
       "22788                                 NaN                                NaN   \n",
       "22789                                 NaN                                NaN   \n",
       "22790                                 NaN                                NaN   \n",
       "\n",
       "       Chae et al. neural distance  \n",
       "0                              NaN  \n",
       "1                              NaN  \n",
       "2                              NaN  \n",
       "3                              NaN  \n",
       "4                              NaN  \n",
       "...                            ...  \n",
       "22786                          NaN  \n",
       "22787                          NaN  \n",
       "22788                          NaN  \n",
       "22789                          NaN  \n",
       "22790                          NaN  \n",
       "\n",
       "[22791 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = pd.read_csv('./data/essential_oil_molecular_distance.csv')\n",
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b18d10",
   "metadata": {},
   "source": [
    "# Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6cd8b77",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Co-occurence In</th>\n",
       "      <th>metrics</th>\n",
       "      <th>original_rank</th>\n",
       "      <th>group</th>\n",
       "      <th>rank</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cFP edit distance</td>\n",
       "      <td>19017.0</td>\n",
       "      <td>non co-occuring</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spearmint, Mentha spicata crispa, extract, Men...</td>\n",
       "      <td>cFP edit distance</td>\n",
       "      <td>9893.0</td>\n",
       "      <td>co-occuring</td>\n",
       "      <td>-1502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cFP edit distance</td>\n",
       "      <td>12857.0</td>\n",
       "      <td>non co-occuring</td>\n",
       "      <td>1462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cFP edit distance</td>\n",
       "      <td>17650.0</td>\n",
       "      <td>non co-occuring</td>\n",
       "      <td>6255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cFP edit distance</td>\n",
       "      <td>12498.0</td>\n",
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       "      <td>1103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>136741</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Chae et al. neural distance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non co-occuring</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136742</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Chae et al. neural distance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non co-occuring</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>136743</th>\n",
       "      <td>Cedar leaf oil, China</td>\n",
       "      <td>Chae et al. neural distance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>co-occuring</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>136745</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Chae et al. neural distance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non co-occuring</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136746 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Co-occurence In  \\\n",
       "0                                                     NaN   \n",
       "1       Spearmint, Mentha spicata crispa, extract, Men...   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "136741                                                NaN   \n",
       "136742                                                NaN   \n",
       "136743                              Cedar leaf oil, China   \n",
       "136744                                                NaN   \n",
       "136745                                                NaN   \n",
       "\n",
       "                            metrics  original_rank            group    rank  \n",
       "0                 cFP edit distance        19017.0  non co-occuring  7622.0  \n",
       "1                 cFP edit distance         9893.0      co-occuring -1502.0  \n",
       "2                 cFP edit distance        12857.0  non co-occuring  1462.0  \n",
       "3                 cFP edit distance        17650.0  non co-occuring  6255.0  \n",
       "4                 cFP edit distance        12498.0  non co-occuring  1103.0  \n",
       "...                             ...            ...              ...     ...  \n",
       "136741  Chae et al. neural distance            NaN  non co-occuring     NaN  \n",
       "136742  Chae et al. neural distance            NaN  non co-occuring     NaN  \n",
       "136743  Chae et al. neural distance            NaN      co-occuring     NaN  \n",
       "136744  Chae et al. neural distance            NaN  non co-occuring     NaN  \n",
       "136745  Chae et al. neural distance            NaN  non co-occuring     NaN  \n",
       "\n",
       "[136746 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz_df = data_df.copy()\n",
    "random_rank = len(viz_df) // 2\n",
    "viz_df = data_df.melt(\n",
    "    id_vars=['Co-occurence In'],\n",
    "    value_vars = [v for v in data_df.columns if v.endswith('distance')], \n",
    "    var_name='metrics', \n",
    "    value_name='original_rank')\n",
    "viz_df['group'] = ['co-occuring' if isinstance(v, str) else 'non co-occuring' for v in viz_df['Co-occurence In']]\n",
    "viz_df['rank'] = viz_df['original_rank'] - random_rank\n",
    "viz_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a8daa44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "YLIM_SIZE = 5000\n",
    "\n",
    "plt.figure(figsize=(3, 4), dpi=500)\n",
    "ax = sns.barplot(\n",
    "    data=viz_df,\n",
    "    x='group',\n",
    "    y='rank',\n",
    "    hue='metrics',\n",
    "    order=['co-occuring', 'non co-occuring'],\n",
    "    hue_order=[\n",
    "        'POM distance',\n",
    "        'cFP edit distance',\n",
    "        'tanimoto distance',\n",
    "    ],\n",
    "    palette=custom_palette,\n",
    ")\n",
    "plt.axhline(0, ls='--', c='k', lw=2)\n",
    "plt.yticks([-4000, -3000, -2000, -1000, 0, 1000, 2000, 3000, 4000],\n",
    "           [-4000, -3000, -2000, -1000, 'random\\nrank', 1000, 2000, 3000, 4000])\n",
    "plt.ylabel('')\n",
    "plt.xlabel('')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.45, 1), facecolor='w', edgecolor='w')\n",
    "plt.legend(loc='upper left', facecolor='w', edgecolor='w')\n",
    "plt.ylim(-YLIM_SIZE, +YLIM_SIZE)\n",
    "ax.set_facecolor((1, 1, 1))\n",
    "plt.gcf().set_dpi(100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eafa029",
   "metadata": {},
   "source": [
    "# Extended Data Figure 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b94e075",
   "metadata": {},
   "outputs": [],
   "source": [
    "carey_viz_df = data_df[data_df['Carey et al. neural distance'].notnull()].copy()\n",
    "for c in carey_viz_df.columns:\n",
    "    if c.endswith('distance'):\n",
    "        carey_viz_df[c] = carey_viz_df[c].rank()\n",
    "random_rank = len(carey_viz_df) // 2\n",
    "carey_viz_df = carey_viz_df.melt(\n",
    "    id_vars=['Co-occurence In'],\n",
    "    value_vars = [v for v in carey_viz_df.columns if v.endswith('distance')], \n",
    "    var_name='metrics', \n",
    "    value_name='original_rank')\n",
    "carey_viz_df['group'] = ['co-occuring' if isinstance(v, str) else 'non co-occuring' for v in carey_viz_df['Co-occurence In']]\n",
    "carey_viz_df['rank'] = carey_viz_df['original_rank'] - random_rank\n",
    "carey_viz_df = carey_viz_df.replace('Carey et al. neural distance', 'neural distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87698af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "hc_viz_df = data_df[data_df['Hallem and Carlson neural distance'].notnull()].copy()\n",
    "for c in hc_viz_df.columns:\n",
    "    if c.endswith('distance'):\n",
    "        hc_viz_df[c] = hc_viz_df[c].rank()\n",
    "random_rank = len(hc_viz_df) // 2\n",
    "hc_viz_df = hc_viz_df.melt(\n",
    "    id_vars=['Co-occurence In'],\n",
    "    value_vars = [v for v in hc_viz_df.columns if v.endswith('distance')], \n",
    "    var_name='metrics', \n",
    "    value_name='original_rank')\n",
    "hc_viz_df['group'] = ['co-occuring' if isinstance(v, str) else 'non co-occuring' for v in hc_viz_df['Co-occurence In']]\n",
    "hc_viz_df['rank'] = hc_viz_df['original_rank'] - random_rank\n",
    "hc_viz_df = hc_viz_df.replace('Hallem and Carlson neural distance', 'neural distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72391ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "chae_viz_df = data_df[data_df['Chae et al. neural distance'].notnull()].copy()\n",
    "for c in chae_viz_df.columns:\n",
    "    if c.endswith('distance'):\n",
    "        chae_viz_df[c] = chae_viz_df[c].rank()\n",
    "random_rank = len(chae_viz_df) // 2\n",
    "chae_viz_df = chae_viz_df.melt(\n",
    "    id_vars=['Co-occurence In'],\n",
    "    value_vars = [v for v in chae_viz_df.columns if v.endswith('distance')], \n",
    "    var_name='metrics', \n",
    "    value_name='original_rank')\n",
    "chae_viz_df['group'] = ['co-occuring' if isinstance(v, str) else 'non co-occuring' for v in chae_viz_df['Co-occurence In']]\n",
    "chae_viz_df['rank'] = chae_viz_df['original_rank'] - random_rank\n",
    "chae_viz_df = chae_viz_df.replace('Chae et al. neural distance', 'neural distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bff251ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 10000x2500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def _barplot(df):\n",
    "    sns.barplot(\n",
    "        data=df,\n",
    "        x='group',\n",
    "        y='rank',\n",
    "        hue='metrics',\n",
    "        order=['co-occuring', 'non co-occuring'],\n",
    "        hue_order=[\n",
    "            'neural distance',\n",
    "            'cFP edit distance',\n",
    "            'tanimoto distance',\n",
    "        ],\n",
    "        palette=custom_palette,\n",
    "        errorbar=None,\n",
    "    )\n",
    "    plt.axhline(0, ls='--', c='k', lw=2)\n",
    "    plt.ylabel('')\n",
    "    plt.xlabel('')\n",
    "\n",
    "plt.figure(figsize=(20, 5), dpi=500)\n",
    "plt.subplot(1, 3, 1)\n",
    "_barplot(carey_viz_df)\n",
    "plt.title(\"Carey et al.\")\n",
    "plt.yticks([-80, -40, 0, 40, 80], [-80, -40, \"random\\nrank\", 40, 80])\n",
    "plt.ylim(-80, 80)\n",
    "plt.legend(loc=\"upper left\", facecolor=\"w\", edgecolor=\"w\")\n",
    "plt.subplot(1, 3, 2)\n",
    "_barplot(hc_viz_df)\n",
    "plt.title(\"Hallem and Carlson\")\n",
    "plt.yticks([-80, -40, 0, 40, 80], [-80, -40, \"random\\nrank\", 40, 80])\n",
    "plt.ylim(-80, 80)\n",
    "plt.legend(loc=\"lower right\", facecolor=\"w\", edgecolor=\"w\")\n",
    "plt.subplot(1, 3, 3)\n",
    "_barplot(chae_viz_df)\n",
    "plt.title(\"Chae et al.\")\n",
    "plt.yticks([-4, -2, 0, 2, 4], [-4, -2, \"random\\nrank\", 2, 4])\n",
    "plt.ylim(-4, 4)\n",
    "plt.legend(loc=\"lower right\", facecolor=\"w\", edgecolor=\"w\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c15d7997",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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